Advanced Model Tuning and Regularization

О курсе

In this course, you will learn how to construct, tune, and optimize predictive models using advanced machine learning techniques. You’ll develop the ability to control model complexity with CART tuning, build powerful ensemble methods such as Random Forests, Gradient Boosting, XGBoost, and LightGBM, and apply L1 and L2 regularization to improve model generalization. You will also master systematic hyperparameter tuning and feature engineering to enhance model performance across diverse datasets. By completing this course, you will be able to diagnose overfitting, select meaningful features, compare model variants, and confidently choose the best-performing approach for real-world prediction tasks. The course is uniquely designed with contributions from industry and academic experts—Edureka, Fractal Analytics, and Illinois Tech—giving you a rare combination of practical methodology, theoretical depth, and hands-on experience. Whether you are strengthening your machine learning foundation or advancing toward specialized modeling roles, this course provides the essential tools and insights needed to build robust, high-performing models with confidence.

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